In many ways, it’s becoming easier to predict sports. Once upon a time, we had to rely on intuition or simply give in to the fact that our pride and prejudice would always point us in a certain direction. But in 2026, there are so many tools out there to analyze data and build predictive models.
Even in the sports betting space, people are learning to model outcomes more accurately.
That’s part of the reason why sports betting is becoming more popular, with Puntit India, specifically, earning thousands of new players despite India being a highly regulated market. When the odds are good and the means of interpreting those odds are more accessible than ever, data-driven decision-making is going to thrive.
But there’s a difference between predicting a game and actually becoming a data analyst. Yes, there are tools that can help us to process information quickly, but in order to properly interpret trends, you can’t just rely on automated software.
If you want to be a data analyst, specifically, it’s important to familiarise yourself with the right programming language. So what programming language do you need to know to be a data analyst in sports, and what’s the best way to learn it?
Two Top Programming Languages for Sport
For sports data analysts, there isn’t just one mandatory programming language, but in practice, there are two that dominate.
The first is Python, which is an industry standard mainly due to the fact that it’s easy to learn – at least compared to most languages – extremely powerful for data work, and packed with libraries used in real analytics jobs.
SQL, however, is equally important, with many analysts using it to get data in the first place. Whether it’s to pull match and player data or filter seasons, leagues, and teams, SQL is a strong querying language that can let you extract exactly what you need, so it’s certainly a skill to consider.
Learning Python
As we just mentioned, part of the reason Python is so popular is because it’s easy to learn, but there are still a few things you need to know about getting started.
Python only becomes really useful in sport when you move beyond the basics and start working with real datasets rather than just writing simple scripts, so that means getting comfortable with libraries like pandas for organizing match data, numpy for handling calculations, and matplotlib for turning raw numbers into something you can actually interpret visually.
A lot of beginners skip this step and focus too much on syntax, but if you do this as a sports analyst, you’re going to quickly hit a ceiling. The real value comes from how you manipulate and interpret data, and that can only be achieved through consistent practice with those libraries.
Learning SQL
In terms of SQL, it’s all about learning how to ask the right questions of a database. Unlike Python, where you’re often working directly with data in scripts or notebooks, SQL is focused on retrieving and organizing information from structured databases.
That means getting comfortable with commands like SELECT, WHERE, JOIN, and GROUP BY, which allow you to isolate specific datasets – for instance, pulling all Premier League matches from a single season, or comparing player stats across different teams – and build the exact set you need.
Once you understand that structure, SQL itself can stop feeling like coding and start feeling more like logic: you’re essentially telling the database exactly what story you want the data to reveal.
Conclusion
Will learning these programming languages help you become a sports analyst overnight? No. But it will give you the foundation needed to start thinking like one – turning that raw information into meaningful insights, and gradually building the skills required to analyze at a professional level.